I am a fourth-year Ph.D. student in Quebec Artificial Intelligence Institute (AKA Mila) and Université de Montréal, advised by Prof. Jian Tang.

Before joining Mila, I got the CS master’s degree from University of Wisconsin-Madison, and was the graduate researcher at Morgridge Institute for Research. During my stay in UW-Madison, I started my first research project and was fortunately advised by Prof. Anthony Gitter, Prof. Yingyu Liang, and Prof. Dimitris Papailiopoulos. Proir to that, I got my bachelor’s degree from Shandong University.

I also want to share some inspiring research values (special thanks to Weiyang):

• Focus on creating novel ideas, not publishing papers
• Follow curiosity and passion, not trends
• Ideas are not owned, but come with debts to those who came before
• Ideas become stronger when shared, discussed and criticized
• Life is surprisingly short, so solve problems that interest and excite you most
• It is good to be quick, but it is more important to be deep
• Think like an amateur, do as an expert
• Last lecture by Prof. Randy Pausch
• [My own words] We are only responsbile for doing fancy research projects and writing fancy papers; for the rest, we can leave them to fate.

Research Topics

• Representation Learning and Distribution Modeling
  • structured/graph/geometric representation learning
  • self-supervised learning
  • multi-task learning
  • deep generative modeling
  • controllable deep generative modeling (editing)
• Drug Discovery
  • small molecule
  • protein
• Learning Dynamics

Selected Publications

Artificial Intelligence Generated Drug (AIGD)

• ProteinDT: A Text-guided Protein Design Framework
  Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar
  [Project Page] [ArXiv] [Code]

• MoleculeSTM: Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
  Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar
  [Project Page] [ArXiv] [Code]
  [NeurIPS AI4Science Workshop 2022]

• GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
  Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou, Jian Tang
  [Project Page] [ArXiv] [Code]
  [NeurIPS GLFrontiers Workshop 2022 Oral]

Transfer Learning

• GeoSSL: Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
  Shengchao Liu, Hongyu Guo, Jian Tang
  ICLR 2023
  [Project Page] [Paper] [ArXiv] [Code]

• GraphMVP: Pre-training Molecular Graph Representation with 3D Geometry
  Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
  ICLR 2022
  [Project Page] [Paper] [ArXiv] [Code] [Slides] [Poster]
  [NeurIPS SSL Workshop 2021]
  [ICLR GTRL Workshop 2022 Spotlight]

• SGNN-EBM: Structured Multi-task Learning for Molecular Property Prediction
  Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
  AISTATS 2022
  [Project Page] [Paper] [ArXiv] [Code] [Slides] [Poster]
  [NeurIPS AI4Science Workshop 2021]

• LBTW: Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
  Shengchao Liu, Yingyu Liang, Anthony Gitter
  AAAI-Student Abstract 2019
  [Paper] [Appendix] [Code] [Poster]

Representation Learning

• AWARE: Attentive Walk-Aggregating Graph Neural Networks
  Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang
  TMLR 2022
  [Paper] [ArXiv] [Code]

• N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
  Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
  NeurIPS 2019 Spotlight
  [Paper] [ArXiv] [Code] [Slides][Poster]
  [NeurIPS MLMM Workshop 2018]

Learning Dynamics

• Bad Global Minima Exist and SGD Can Reach Them
  Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
  NeurIPS 2020
  [Paper] [Code] [Slides] [Poster] [Video/Audio, NeurIPS 2020]
  [ICML Deep Learning Phenomena Workshop 2019 Oral]

Interests

• Coding. Like ACM-ICPC and Capture the Flag.
• Video Editing. Quite cool stuff.
• Reading. Novels are my favourite, but also history and computer books.
• Music. Classical music are perfect match for coding.